Your AI models are failing in production—Here’s how to fix model selection

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more

Enterprises need to know if the models that power their applications and agents work in real-life scenarios. This type of evaluation can sometimes be complex because it is hard to predict specific scenarios. A revamped version of the RewardBench benchmark looks to give organizations a better idea of a model’s real-life performance.

The Allen Institute for AI (Ai2) launched RewardBench 2, an updated version of its reward model benchmark, RewardBench, which they claim provides a more holistic view of model performance and assesses how models align with an enterprise’s goals and standards.

Inside the Cybersecurity-First AI Model

Ai2 built RewardBench with classification tasks that measure correlations through inference-time compute and downstream training. RewardBench mainly deals with reward models (RM), which can act as judges and evaluate LLM outputs. RMs assign a score or a “reward” that guides reinforcement learning with human feedback (RHLF).